Analysis of in-situ stress field by using a visual BP neural network

2010 ◽  
pp. 287-290
Author(s):  
Li Yongsong ◽  
Ai Kai ◽  
Liu Yuankun
Keyword(s):  
Neural Network ◽  
Stress Field ◽  
Bp Neural Network ◽  
In Situ Stress ◽  
In Situ Stress Field

scholarly journals Inversion Method of Initial In Situ Stress Field Based on BP Neural Network and Applying Loads to Unit Body

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Xiaopeng Li ◽  
Xuejun Zhou ◽  
Zhengxuan Xu ◽  
Tao Feng ◽  
Dong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Stress Field ◽  
Principal Stress ◽  
Bp Neural Network ◽  
In Situ Stress ◽  
Inversion Method ◽  
Linear Regression Method ◽  
Underground Engineering ◽  
In Situ Stress Field

The initial in situ stress field is the fundamental factor causing the deformation and failure of underground engineering and is an important basis for the feasibility analysis, design, and construction of underground engineering. However, it is difficult to obtain the whole in situ stress field of large-scale underground engineering in difficult and dangerous areas by field measurement. In view of the fact that the measured in situ stress components (σxx, σyy, σzz, τxy, τxz, τyz) of Sichuan-Tibet Railway in China are linear with the buried depth, a method is proposed to solve the in situ stress by applying corresponding loads to all unit bodies in the calculation area based on BP neural network and FLAC3D. Through this method, the in situ stress of the tunnel is inverted. The results show that both the maximum principal stress and minimum principal stress increase with the increase of buried depth, and when the tunnel passes through faults or anticlines, the main stress will suddenly drop. Furthermore, compared with the results of the multiple linear regression method, it is found that the proposed method has higher accuracy; especially for the simulation of the maximum horizontal principal stress and vertical stress, the average relative error is reduced by 26.44% and 77.27%, respectively. The research in this paper can provide a new idea for the initial in situ stress inversion of engineering.

Inverse Analysis of Regional In Situ Stress Prediction Based on Multi-Points Measurement and BP Neural Network

2014 ◽  
Vol 510 ◽  
pp. 226-231 ◽  
Author(s):  
Wei Qun Liu ◽  
Ting Song ◽  
Yu Shou Li ◽  
Shu Fei Zheng ◽  
Jing Yang
Keyword(s):  
Neural Network ◽  
Stress Field ◽  
In Situ Stress ◽  
Engineering Problem ◽  
Regional Stress ◽  
Geological Conditions ◽  
Stress Prediction ◽  
Stress Estimation ◽  
In Situ Stress Field

Based on the measurement of in-situ stress and engineering-geological conditions, we built computing models with pre-exerting boundary loads and simulated the regional stress field involved. Boundary loads can be approximately determined by use of the multiple linear regressions, and be further optimized with the artificial neural network. By calculation, the corresponding initial in-situ stress field can reach ideal accuracy. As an example, we inversely analyzed an engineering problem in Chinese Wo-bei mine. The results shows that the simulation can meet the point measurement very well, and the regional-stress estimation may play an important role in engineering.

scholarly journals Local Stress Field Correction Method Based on a Genetic Algorithm and a BP Neural Network for In Situ Stress Field Inversion

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tianzhi Yao ◽  
Zuguo Mo ◽  
Li Qian ◽  
Jianhua He ◽  
Jianhai Zhang
Keyword(s):  
Stress Field ◽  
Bp Neural Network ◽  
Local Stress ◽  
In Situ Stress ◽  
Regression Method ◽  
Geological Conditions ◽  
Multiple Regression Method ◽  
In Situ Stress Field ◽  
Local Stress Field

The in situ stress field is the fundamental factor causing deformation and damage in geotechnical engineering, so it is the main basis for underground engineering design and excavation. However, it is difficult to accurately obtain the in situ stress through most existing inversion methods in areas with complex geological conditions. For the problem of a relatively discrete and nonlinear relationship of measured stress in the Yebatan Hydropower Station area, a new in situ stress inversion method called the local stress field correction (LSFC) method combining a genetic algorithm (GA), backpropagation (BP) neural network, and submodel method is proposed. The inverted in situ stress results produced by this method show that the distribution of in situ stress is greatly influenced by tectonic movements in the Yebatan area, there is no obvious linear relationship with depth, and the stress release phenomenon occurs at the faults. By comparison with the multiple regression method, it is found that the method still has high inversion accuracy under complex geological conditions, and the average relative error of LSFC inversion results is 17.05%, which is much lower than the value of 43.58% via the multiple regression method. Therefore, the LSFC method can be used for the inversion of in situ stress in complex geological regions and provide a reference for engineering design and construction.

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